Data should be placed at the most cost- and performance-effective tier in the storage hierarchy. While performance and cost decrease with distance from the CPU, the cost/performance trade-off depends on how efficiently data can be moved across tiers. Log structuring improves this cost/performance by writing batches of pages from main memory to secondary storage using a conventional block-at-a-time I/O interface. However, log structuring incurs overhead in the form of recovery and garbage collection. With computational Solid-State Drives, it is now possible to design a storage interface that minimizes this overhead. In this paper, we offload log structuring from the CPU to the SSD. We define a new batch I/O storage interface and we design a Flash Translation Layer that takes care of log structuring on the SSD side. This removes the CPU computational and I/O load associated with recovery and garbage collection. We compare the performance of the Bw-tree key-value store with its LLAMA host-based log structuring to the same key-value software stack executing on a computational SSD equipped with a batch I/O interface. Our experimental results show the benefits of eliminating redundancies, minimizing interactions across storage layers, and avoiding the CPU cost of providing log structuring.

数据应放置在存储层次结构中最具成本效益和性能最有效的一层。尽管性能和成本会随着与CPU的距离的增加而降低，但成本/性能的取舍取决于数据在各层之间的移动效率。日志结构通过使用常规的一次块I / O接口将页面的批次页面从主存储器写入辅助存储器，从而提高了成本/性能。但是，日志结构以恢复和垃圾回收的形式产生开销。利用计算固态驱动器，现在可以设计一个存储接口，以最大程度地减少这种开销。在本文中，我们将日志结构从CPU卸载到SSD。我们定义了一个新的批处理I / O存储接口，并设计了一个Flash转换层，该层负责SSD侧的日志结构。这消除了与恢复和垃圾回收相关的CPU计算和I / O负载。我们将Bw树键值存储的性能与其基于LLAMA主机的日志结构与在配备批处理I / O接口的计算SSD上执行的相同键值软件堆栈进行了比较。我们的实验结果表明，消除冗余，最小化存储层之间的交互以及避免提供日志结构的CPU成本的好处。